Publications (8)
BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation
Rutav Shah, Albert Yu, Yifeng Zhu +2
To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interactin…
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Homer Walke, Jonathan Yang, Albert Yu +4
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires…
Natural Language Can Help Bridge the Sim2Real Gap
Albert Yu, Adeline Foote, Raymond Mooney +1
The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image s…
Exploring Evolving Plants as Interacting Particles in a Randomly Generated Heterogeneous Environment
Alexander, Khazatsky, Albert Yu +2
We model evolution of plants in a world, made up of different locations, with multiple environments (mutually exclusive and collectively exhaustive subsets of locations). Each envi…
Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation
Albert Yu, Chengshu Li, Luca Macesanu +4
Effective robotic systems for long-horizon human-robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assist, and understanding…
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks
Albert Yu, Raymond J. Mooney
Demonstrations and natural language instructions are two common ways to specify and teach robots novel tasks. However, for many complex tasks, a demonstration or language instructi…